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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.
Topic 2	Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.
Topic 3	OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.
Topic 4	Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.
Topic 5	Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?

A. Use command-line variables
B. Configure a block volume
C. Configure a compute shape
D. Use environmental variables

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

NEW QUESTION # 49
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?

A. Entity - Log Association
B. Log Group - Source Association
C. Source - Entity Association
D. Log - Log Group Association

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

NEW QUESTION # 50
You are running a pipeline in the OCI Data Science service and want to override some of the pipeline's default settings. Which of the following statements about overriding pipeline defaults is true?

A. Pipeline defaults can be overridden before starting the pipeline execution.
B. Pipeline defaults can be overridden only during pipeline creation.
C. Pipeline defaults can be overridden only by the Administrator.
D. Pipeline defaults cannot be overridden once the pipeline has been created.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.
* Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.
* Evaluate Options:
* A: False-Overrides can occur post-creation, before running.
* B: False-Any authorized user, not just admins, can override defaults.
* C: True-Settings can be adjusted before execution starts.
* D: False-Defaults can be changed post-creation, pre-execution.
* Conclusion: C is correct as it reflects the flexibility of pipeline configuration.
OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B).
(Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").

NEW QUESTION # 51
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

A. Setup volumes in a RAID1 configuration
B. Limit delete permissions
C. Enable client-side encryption
D. Enable server-side encryption
E. Enable Versioning

Answer: B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

NEW QUESTION # 52
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create a new job every time you need to run your code and pass the parameters as environment variables
B. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
C. Create your code to expect different parameters as command-line arguments, and create a new job every time you run the code
D. Create a new job by setting the required parameters in your code, and create a new job for every code change

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
* Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
* Evaluate Options:
* A: New job per run with env vars-Redundant job creation, inefficient.
* B: New job per run with args-Similarly inefficient due to repeated setup.
* C: Hardcode params, new job per change-Highly inefficient, requires code edits.
* D: Single job, flexible params via env vars or args-Efficient, reusable-correct.
* Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice.
* Conclusion: D is correct.
OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs.
Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

NEW QUESTION # 53
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Free PDF Quiz Oracle - 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional High Hit-Rate Valid Vce Dumps

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q48-Q53):

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